The magnetic signature of an urban environment is investigated using a
geographically distributed network of fluxgate magnetometers deployed in and
around Berkeley, California. The system hardware and software are described
and initial operations of the network are reported. The sensors measure
vector magnetic fields at a 3960 Hz sample rate and are sensitive to
0.1 nT/Hz. Data from individual stations are
synchronized to ±120µs using global positioning system (GPS) and computer system clocks
and automatically uploaded to a central server. We present the initial
observations of the network and preliminary efforts to correlate
sensors. A wavelet analysis is used to study observations of the urban
magnetic field over a wide range of temporal scales. The Bay Area Rapid
Transit (BART) is identified as the dominant signal in our observations,
exhibiting aspects of both broadband noise and coherent periodic features.
Significant differences are observed in both day–night and weekend–weekday
signatures. A superposed epoch analysis is used to study and extract the BART
signal.

The study of fluctuating magnetic fields, commonly referred to as
magnetometry, has found widespread application in a variety of disciplines.
Understanding geomagnetic fields are important to
global-positioning-system-free
(GPS-free) navigation, radiation hazard prediction, atmospheric modeling, and
fundamental geophysics research. Additionally, measurements of the auroral
magnetic field are necessary in testing models for space-weather prediction,
aiming to mitigate hazards from solar storms
(Angelopoulos, 2008; Peticolas et al., 2008; Harris et al., 2008). High-cadence (sampling
rate) magnetic field measurements have been used for magnetic anomaly
detection (MAD), which has numerous applications in naval defense and
unexploded ordnance detection (Sheinker et al., 2009). Geographically
distributed magnetometers operating at high sample rates have been used in
atmospheric science to measure the global distribution of lightning strikes
using the Schumann resonance (Schlegel and Füllekrug, 2002). Recently, multi-satellite
space missions have used magnetometry to provide multi-point observations of
the dynamic evolution of plasmas on electron scales (Burch and Phan, 2016; Phan et al., 2018).

Many advancements in magnetometry have been driven by progress in the design
of high-precision sensor networks. Time-synchronized sensor networks are
currently being implemented in diverse disciplines such as the search for dark
matter (Pustelny et al., 2013; Afach et al., 2018) and localization and monitoring of
wildlife in ecology (Wang et al., 2005); perhaps the most notable recent
accomplishment of network design is the observation of gravitational waves
associated with black hole mergers by the Laser Interferometer
Gravitational-Wave Observatory (LIGO) and Virgo Interferometer collaborations
(Abramovici et al., 1992; Abbott et al., 2016, 2017). Indeed, sensor networks have long been used in
geophysics to monitor geomagnetic dynamics: e.g., SuperMAG, an international
consortium of magnetometer arrays, comprises approximately 300 sensors
operated by numerous organizations, providing uniformly processed data in a
common coordinate system and time base (Gjerloev, 2009, 2012).

Here we report on the development of a synchronized magnetometer network in
Berkeley, California, for the purpose of studying urban magnetic fields over
spatiotemporal scales not previously studied. By applying proven
techniques of network design and magnetometry, we aim to complement the
growing field of observational urban science and informatics. Only recently
have investigators been able to accumulate and analyze data with adequate
spatial and temporal granularity to characterize the dynamic evolution of a
city. Recent work has shown that broadband visible observations at night can
identify patterns of light that can be used to measure total energy
consumption and public health effects of light pollution on circadian rhythms
(Dobler et al., 2015). Measurements of urban lighting made faster than
∼120 Hz allow for phase change detection of fluorescent and
incandescent flicker, which may be used as a predictor for power outages
(Bianco et al., 2016). In addition, infrared hyperspectral observations can
determine the molecular content of pollution plumes produced by building
energy consumption, providing a powerful method for environmental monitoring
of cities (Ghandehari et al., 2017). In tailoring a synchronized network of
magnetometers for an urban area, we explore whether dynamic signatures of
urban magnetic fields can contribute to the growing discourse surrounding
urban science and informatics.

Our magnetometer array, currently consisting of four sensors operating at a
3960 Hz sample rate with high-precision timing (120µs) for
correlation analysis, will make sustained measurements of urban magnetic
fields over years, observing dynamic magnetic signatures characterizing a
city. Figure 1 shows a map of Berkeley with a sample network
configuration. Our hardware, software, and instrumental techniques are
described in Sect. 2. Initial network observations are
presented in Sect. 3. Observations taken from multiple
geographically separated stations are analyzed and compared in
Sect. 3.1. A wavelet analysis is introduced in
Sect. 3.2 to provide an analysis of the different
spatiotemporal scales present in the urban magnetic field. Initial results of
multi-station correlations are presented. In Sect. 4, we
present an initial method to isolate the BART signal.

Figure 1Map of Berkeley and location of the stations. The colored pins
identify the locations of the magnetometers in our network. The black line
shows the different paths of the BART trains. The shortest distance from each
magnetometer station to the nearby BART line is represented by the dashed
lines. Stations 1 to 4 are respectively located 1, 0.13, 2, and 0.36 km from
the BART rail.

2.1 Instrumentation and hardware

Each station consists of a commercially available fluxgate magnetometer, a
general-purpose laptop computer, and an inexpensive GPS receiver. We
emphasize our preference for commercially available hardware and
additionally highlight the adoption of timing techniques from the Global
Network of Optical Magnetometers for Exotic (GNOME) physics project
(Pustelny et al., 2013). Our approach, which avoids bulky and expensive
hardware in favor of consumer components wherever possible, reduces the cost
of the acquisition system and enables portability through battery operation.
However, achieving the desired timing precision (∼120µs)
with affordable commercial hardware requires a customized timing
synchronization algorithm.

Each station is controlled by a computer (PC, ASUS X200M) running the Windows
10 operating system (OS). The PC acquires magnetic field measurements from a Biomed eMains 24 bit
Universal Serial Bus (USB) data acquisition device (DAQ) and timing data from the
GPS receiver (Garmin 18x LVC). The DAQ continuously samples the Biomed eFM3A
fluxgate magnetometer at a sample rate of 3960 Hz. Absolute timing data are
provided once per second by the GPS receiver, which is connected through a
high-speed RS232-to-USB converter (SIO-U232-59). The GPS pulse-per-second
signal is routed to the computer through the carrier detect (CD) pin of the
RS232 converter with 1 µs accuracy. Data from the DAQ arrive in
packets of 138 vector samples approximately every 35 ms. The data are
recorded together with the GPS information and the computer system clock and
uploaded via wireless internet to a shared Google Drive folder.

2.2 Time synchronization

Time intervals between the GPS updates are measured by the computer system
clock (performance counter), running at ≈2.5 MHz. A linear fit
model is used to determine the absolute system time relative to the GPS. The
GPS timing data from the previous 120 s are used to determine linear fit
parameters. When a magnetic field data packet is received, the acquisition
system records the performance counter value. The packet time tag is
determined by interpolating the linear fit GPS time to the performance
counter value. Typical jitter of the inferred time stamps is 120 µs and is limited by the USB latency (Korver, 2003).

Due to OS limitations some data packets are not processed immediately by the
PC. Packets may be delayed by several milliseconds before arriving to the
data acquisition software. The number of delayed packets depends on the
OS load. During normal operation of a magnetometer, about 3 % of packets
are delayed. The intervals between GPS pulses and packet arrival times
are measured with the performance counter in order to identify the delayed
packets. Any GPS pulses that deviate more than 50 µs from the
expected arrival time are discarded from the linear fit model. When a data
packet arrives with a 200 µs deviation from the expected time, the
associated time stamp is replaced with the expected arrival time inferred
from the linear fit.

2.3 Performance characterization

To characterize sensor and data acquisition system timing performance, the
four sensors are simultaneously placed in a single Helmholtz coil system
driven by a pulse generator. Each sensor is sampled by a unique DAQ and PC,
ensuring that the time characterization accounts for all delays associated
with signal processing and transmission through the system. Sensors are
stimulated with a 2 µT amplitude square wave, with a period of
200 ms and duty cycle of 50 % (Fig. 2a). Figure 2 represents the data before and after the application
of the timing correction algorithm. The inset in Fig. 2a
demonstrates how a delay in retrieving a data packet disrupts the timing of
the field pulses. When a data packet is delayed, the magnetic field samples
are distributed over a slightly larger time period, causing a shift in the
observed time of the square wave zero crossing relative to the other sensors.
Figure 2b shows the time discrepancies between the interpolated
and expected square wave zero crossings. The zero crossings are recorded with
a 120 µs standard deviation from the expected interval; however, a
large number of outliers with up to 10 ms of discrepancy exist.
Figure 2c shows the histogram of the data from
Fig. 2b, where the red curve represents the best-fit Gaussian.
After the timing correction algorithm is applied to the data, time stamps
associated with outlier packets are replaced with the expected arrival times
(Fig. 2d, e, f). The remaining jitter is Gaussian distributed
with an error of 120 µs.

Figure 2Characterization measurement of the time synchronization algorithm.
The four magnetometers are subjected to a square-wave-modulated magnetic
field. Panels (a–c) show measurements with unprocessed
time tags. Panels (d–f) show the time tags after processing.
(a) Time series of the four magnetometer traces. The inset shows a
discrepancy in magnetometer 4 at the zero crossing. (b) The
difference of the mean zero-crossing time of the square wave and the
individual zero-crossing time for each magnetometer for 2 min of data.
(c) Histogram of the data in (b), with the red curve
representing the best-fit Gaussian. While most zero-crossing events have the
correct timing within the 120 µs standard deviation of the
Gaussian fit, there are a significant number of outliers. Panels (d),
(e), and (f) show the same data after implementing the time
synchronization algorithm.

Figure 3 shows the instrumental noise floors for each vector axis
of the magnetometers. Data were obtained in a two-layer μ-metal shield
for approximately 35 min (223 samples at the 3960 Hz sample rate).
Data were separated into an ensemble of 64 individual intervals of 217
samples. Power spectral densities were calculated for each interval, with the
noise floor taken as the ensemble average of the interval spectra. The noise
floor varies between individual axes, with the most noise observed on the
Z axis. For all three directions, the noise floor is constant between ∼2 and 700 Hz. Narrowband spectral features from 60 Hz and harmonics are
easily observed in the data. For frequencies above 1 Hz, the noise floor is
uniformly below 0.1 nT/Hz. The peak observed in the
noise floor, predominately in the Z and Y channels between ∼300
and ∼1500 Hz, is likely due to the operation of the fluxgate
electronics.

Figure 3Instrumental noise floors for each vector axis of a Biomed magnetometer.

3.1 Multi-station analysis of urban magnetic fields

The spatiotemporal behavior of ultralow-frequency electromagnetic fields
throughout the San Francisco Bay Area has led to the identification of the
Bay Area Rapid Transit (BART) as a source of electromagnetic noise
(Fraser-Smith and Coates, 1978). Subsequent measurements at a distance of 100 m
from
BART suggested periodic bursts of magnetic activity with roughly the periodicity
of the BART train (Ho et al., 1979). Given its observability, reliable
periodicity, and the presence of multi-scale signatures (e.g., individual train
periodicity versus daily operation schedule), the BART is a useful tool to
understand the operation of a magnetometer array in an urban area.

Many magnetic signatures associated with urban activity occur at frequencies
at which GPS alone provides adequate timing. To observe the spatiotemporal
effect of BART in the urban magnetic field, we decimate our high-cadence data
to a 1 Hz sample rate using a 3960-element moving average low-pass filter
(−3 dB cutoff at ≈0.44 Hz) to address aliasing. Low-cadence data
can provide adequate time resolution and bandwidth for correlating
multi-station observations of urban magnetic fields over many days and
months. In the subsequent analysis, only data from stations 2, 3, and 4 are
used for multi-station comparisons; station 1 served as an engineering unit
at the time.

Figure 4 shows the fluctuating scalar magnetic field magnitude of
three stations on Sunday, 20 March 2016 (PDT). Mean fields for each station
are subtracted from the total magnitude. A magnetically quiet period
corresponding to BART nonoperating hours is consistent with previous
observations of the urban magnetic field (Fraser-Smith and Coates, 1978). Each
panel additionally shows 1 min average magnetic field observations from
the USGS station in Fresno, CA. Figure 4a demonstrates a general
agreement between our observations and the USGS data. Figure 4b
shows that urban fluctuations dominate the daytime magnetic field.
Additionally, Fig. 4c shows a subset of the data from
10:00 to 11:00 PDT, revealing several synchronous spikes in each sensor.

Figure 4Magnetic field magnitudes for three stations at a cadence of one
sample per second
on 20 March 2016. Panel (a)
shows close agreement between the geomagnetic field measured by a USGS
station in Fresno, CA, and sensor 3. Panel (b) shows 24 h scalar
magnitudes (DC values were adjusted for plotting purposes) for the three
active sensors; the magnitude of fluctuations decreases with increasing
distance from the BART train line. The grey bar represents 1 h of data
from 10:00 to 11:00 PDT; a better visualization of that region can be
seen in panel (c). The 1 min averaged USGS geomagnetic field
data are shown in each plot as a black line.

Figure 5 shows distributions of observed magnitude fluctuations
binned at 10−3µT. The record naturally separates into two
intervals, corresponding to the hours when BART trains are running (roughly,
from 07:55 to 01:26 PDT) and hours when BART trains are inactive. We
refer to these intervals as “daytime” and “nighttime”, respectively.
Different characteristics are observed for daytime and nighttime
distributions. The nighttime distribution functions appear as a superposition
of several individual peaks. The standard deviations, indicative of a typical
fluctuation amplitude, of the measurements are given in order of increasing
amplitude as σ3=0.009µT, σ4=0.026µT, and σ2=0.072µT. Computing the amplitude of a
typical nighttime fluctuation using the average of standard deviations
computed using a 20 min sliding window throughout the night significantly
reduces the estimated fluctuation amplitudes
〈σ3〉=0.001µT,
〈σ4〉=0.004µT, and
〈σ2〉=0.014µT. This suggests that the
magnetic field fluctuations during the night are dominated by low-amplitude
fluctuations, with occasional large jumps in the field leading to a
multi-peaked distribution function. Different numbers of discrete peaks
(jumps) are observed in the sensors, suggesting that they may not be related
to globally observable magnetic events, e.g., the BART.

Figure 5Distributions for 24 h, daytime, and nighttime magnetic field
observations with typical fluctuation amplitudes, σ, computed for each
period. Additionally, the amplitude of typical nighttime fluctuations is
computed using a 20 min sliding window, 〈σ〉, and
demonstrates that nighttime observations are nonstationary with several
discrete peaks. Daytime fluctuations follow broad distributions. The variance
of the distributions increases as the distance from the BART train line
decreases.

From Fig. 1, it is clear that both the daytime and nighttime
variance of the distributions increases as distance to the BART rail
decreases, suggesting that the background noise level observed in each
station is set by the distance to BART, even when the trains are not running.
Identifying other anthropogenic fields (for example, traffic) is complicated
by the presence of the large BART signal. Accordingly, identifying the
signatures of additional urban sources requires a thorough characterization
of the magnetic background generated by BART.

3.2 Time–frequency (wavelet) analysis

Localizing the temporal distribution of spectral power requires simultaneous
analysis in both time and frequency domains. To investigate the
time–frequency distribution of low-frequency fluctuations associated with
BART we implement a continuous wavelet transform (CWT) using Morlet wavelets
(Torrence and Compo, 1998). The unnormalized Morlet wavelet function ψ(τ)
is a Gaussian-modulated complex exponential,

(1)ψ(τ)=π1/4eiω0τe-τ22,

with nondimensional time and frequency parameters τ and ω0. A
value of ω0=6 is commonly used across disciplines
(Podesta, 2009; Torrence and Compo, 1998; Farge, 1992). The CWT of the magnetic field
B is defined by the convolution of the time series with a set of
self-similar wavelets, scaled by factor s and normalized to maintain unit
energy at each wavelet scale.

(2)W(s,t)=∑i=0N-1Bx(ti)ψ(ti-ts)

We implement a wavelet transform with 130 scales logarithmically spaced
at 2≤s≤16384, corresponding to a frequency range of
0.58 mHz ≲f<≲0.47 Hz. Full-day wavelet power spectral
densities |W(s,t)|2 (in units of µT2∕Hz) for stations
3 and 4 on Sunday, 20 March 2016 (PST) are displayed in Fig. 6.
These spectrograms prominently display the quiet nighttime period across all
scales. Strong power is observed in scales corresponding to a 20 min period
(0.833 mHz) and associated higher harmonics. This 20 min period
coincides with the Sunday BART timetable on the geographically closest BART
line (Richmond–Fremont). The black lines display the region subject to edge
effects, known as the cone of influence (COI), corresponding to the
e-folding time for the wavelet response to an impulse.

Figure 6Time–frequency analysis of scalar magnetic field. Continuous wavelet
transform power spectral densities (µT2∕Hz) for stations
3 (a) and 4 (b). The spectrograms reveal power in several
bands, including the 0.83 mHz frequency corresponding to a 20 min train
period, common to both sensors. White dashed lines show the frequency range
of a brick-wall filter (0.7–10 mHz) applied to isolate these narrowband
features. Black lines show the region where edge effects may impact the
wavelet transform, frequently called the cone of influence.

A brick-wall band-pass filter (i.e., unity gain in the passband, full
attenuation in the stop band) between 0.7 and 10 mHz is applied to each sensor
to isolate the range of observed spectral features.
Figure 7a shows the band-passed time series for 10:00–11:00 PDT on
20 March 2016. The band-passed time series are normalized to their maximum
values for the purpose of visualization. These observations suggests that
stations 3 and 4 are correlated with each other and respectively
anticorrelated with station 2. This is verified by
Fig. 7b, which shows the cross-correlation coefficients
Cij(τ) calculated for the band-passed 24 h time series:

where N is the record length, τ is a translation between time series,
and n is the sample index (Bendat and Piersol, 1990). The phase correspondence
between the stations is consistent with the distribution of stations
east–west of the BART line (Fig. 1) and suggests strong
azimuthal symmetry around the BART rail. Future work will aim to determine
the multiple components (e.g., line current, dipole, quadrupole)
corresponding to the BART field.

Figure 7(a) Band-passed 0.7–10 mHz time series for all three stations on 20 March 2016
between 10:00 and 11:00 PDT. (b) The correlation
coefficients between pairs of stations as a function of lag. Stations 3 and 4
are highly correlated (in phase), while station 2 is respectively
anticorrelated (out of phase).

Figure 8BART night signatures. Continuous wavelet transform of
magnetic field magnitude data at one sample per second from station 2 on Wednesday,
16 March 2016 (a) and Sunday, 20 March 2016 (b). The
nighttime signature is significantly shorter in the data taken on Wednesday;
this corresponds to BART operating hours. Additionally, the strong
power bands observed on Sunday are not present in the Wednesday data.

Figure 8 shows full-day wavelet spectral densities for station 2
on both Wednesday, 16 March 2016 and Sunday, 20 March 2016. The quiet BART
night is much shorter on Wednesday, corresponding to different weekend and
weekday timetables. Additionally, Fig. 8a demonstrates the
absence of a strong 20 min spectral component in the Wednesday data. The
increase in spectral complexity is consistent with increases in train
frequency, an additional active BART line, and variability associated with
schedule changes for commuter hours. Previous efforts to capture period
signatures associated with the BART found bursts of activity corresponding
approximately to the train schedule but with an irregular variation in the
waveform (Ho et al., 1979). In contrast, our observations reveal a highly
regular signature, with multiple spectral components, occurring at the BART
train period. The periodicity of the coherent BART signature enables the
identification and extraction of the time series waveform associated with
BART operation.

Previous attempts have been made to identify and remove transient features
associated with BART from geomagnetic time series using wavelets
(Liu and Fraser-Smith, 1998). Our observations of a highly periodic BART signature
suggest that statistical averaging over observations may be used to remove
the periodic behavior. The periodic 20 min signature observed in the Sunday,
20 March 2016 time series from station 2 can be extracted using the technique
known as superposed epoch analysis (Singh and Badruddin, 2006). We identified 46 sharp
peaks in the magnetic field occurring with an approximate 20 min period
(e.g., Fig. 4). From these 46 peaks, an ensemble [X(t)i] of
intervals is constructed comprising the 3 min preceding and 17 min
succeeding each individual peak. Averaging over the ensemble of intervals
X¯(t)=1N∑i=0N-1Xi(t) reveals a coherent signature
with an approximate 20 min period; see Fig. 9a. The periodic
signal observed in the data has the form of a sharp discrete peak of ≈1µT, followed by an oscillation with a period on the order of
several minutes. A quantitative comparison between each member of the
ensemble and the extracted coherent structure is obtained through the Pearson
correlation.

Figure 9Extraction of BART signal. (a) The 20 min periodic signal of
the BART extracted from an ensemble average of 46 intervals from station 2.
(b) Comparison of the extracted average signal with an hour of
observations from station 2 taken from 09:00 to 10:00 PDT.
(c) Comparison of extracted average signal with an interval
containing a global magnetic anomaly likely due to variation in BART
operation.

The average correlation between the extracted signal and observed data is
ρ¯=0.7, with ρi ranging from 0.1 to 0.85. The cross-correlation indicates the fraction of power in each interval derived from the
average signature. Figure 9b demonstrates high correlation
between the extracted average signal with an hour of observations taken from
09:00 to 10:00 PDT. Figure 9c demonstrates an interval of
data (with ρ=0.17) that includes a transient feature observed in
multiple stations, likely due to transient variation in the BART operation.
By extracting periodic magnetic signatures of BART, we enable the
identification of globally transient events associated with BART operation
that can be used to differentiate local anomalous behavior due to other
urban signatures. Measurements from a single sensor allow us to identify
events that deviate from the correlated periodic observations; our future
work will employ the full network of magnetometers to identify
spatiotemporally correlated signals, allowing for the identification of magnetic
fluctuations local to each sensor.

An array of four magnetometers has been developed with a sampling rate of
∼4 kHz and sensitivity better than 0.1nT/Hz
for the purpose of monitoring urban magnetic fields. The array has been
deployed around Berkeley, CA, and subsequent work will report on
observations taken in New York City. This array is sensitive to both low-frequency
variations in the Earth's geomagnetic field and a variety
of anthropogenic sources: currents associated with BART, traffic, and 60 Hz
power lines. The broadband spectra of the urban magnetic field are dominated
by the BART train system, which also generates coherent narrowband spectral
features. Significant variations in spectral signatures are observed between
weekends and weekdays, corresponding to differences in the BART train
schedule. During the hours for which BART is nonoperational, broadband noise
is significantly decreased and agreement with the USGS magnetic field
measurements is observed. However, the nighttime field still contains
features not attributable to geophysical activity. Further study is required
to determine the nature and sources of these features.

These observations rely on the implementation of the network presented in
Sect. 2. Our network relies on low-cost commercial hardware
and a customized time synchronization algorithm that allows for the
cross-correlation of the sensors at high frequencies. This algorithm
additionally corrects for latency issues associated with the use of
commercial, non-precision hardware. Our synchronization procedure, which has
been verified through simultaneous simulation of sensors, is precise to
≈120µs. Though many sources of urban magnetic fields
operate at much slower timescales, this precision enables studies of high-frequency urban magnetic anomaly detection and sensor cross-correlation.
Additionally, this synchronization algorithm may be implemented across other
sensor networks built with cost-effective hardware.

We provide a proof-of-concept deployment of what, to our knowledge, is the
first synchronized network of magnetometers specifically designed for
observing the effects of human activity on the magnetic field in an urban
environment. Further development of algorithms to characterize and extract
the BART signal, as well as other magnetic signatures, is necessary. These
algorithms may take advantage of other data sources – e.g., real-time BART
arrival–departure data – and machine-learning techniques. In analyzing
magnetic observations of a city, we demonstrate that urban magnetic fields
can reflect identifiable aspects of human activity; further work will aim to
identify other sources of urban magnetic fields with multi-scale signatures
(e.g., traffic and energy consumption). This work may prove useful for the
identification and reduction of noise in geophysical sensor networks that
measure magnetic fields and seismic activity. Several additional courses for
future work are evident: the study of high-frequency signals such as the
60 Hz power line; exploration of the long-term behavior of magnetic fields
taken from single station observations; and comparative measurements of urban
magnetic fields between cities.

The datasets generated and analyzed during the current
study are available from the corresponding author on reasonable request.
Public release of the data has not been approved at this time. Our time
filtering algorithm and data acquisition software are publicly available on
GitHub at https://github.com/lenazh/UrbanMagnetometer (last access:
3 May 2018).

EZ, AW, and VD were responsible for hardware development. EZ, AW, VD, and TAB
participated in network characterization. TAB conducted
analysis of magnetometer data. AW, VD, and TAB generated figures. VD, TAB,
and EZ contributed most portions of the paper. DB, JW, GD, CP, and AW
generated the initial concept for the network and study. DB and JW were
responsible for project management and coordination. All authors participated
in discussions, the direction of the work, and review of the paper.

We are grateful to Brian Patton for his contributions in the early stages of
the project. The views expressed in the publication are those of the authors
and do not imply endorsement by the Department of Defense or the National
Geospatial-Intelligence Agency. Gregory Dobler's work has been partially supported by
a James S. McDonnell Complex Systems Scholar Award.

We highlight the development of a low-cost portable sensor array to study magnetic fields in urban areas. Recent advancements in urban science have demonstrated significant utility in characterizing a city based on physical measurements. Magnetic fields of cities are characterized by significant noise; in the case of the San Francisco Bay Area, this noise is dominated by the BART train system. We demonstrate an ability to identify and extract BART noise from the urban magnetic environment.

We highlight the development of a low-cost portable sensor array to study magnetic fields in...